Stephanie Song, formerly of the Corporate Development and Ventures team at Coinbase, was often frustrated by the amount of due diligence she and her team had to complete on a daily basis.
“Analysts burn the midnight oil doing hundreds of hours of work that no one wants to do,” Song told TechCrunch in an email interview. “At the same time, funds are investing less and looking for ways to make their teams more efficient while reducing operating costs.”
Motivated to find a better way, Song teamed up with two former Coinbase colleagues, Brian Fernandez and Anand Chaturvedi, to launch Dili (never mind the capital of East Timor), a platform that which seeks to automate critical investments. Portfolio management initiatives for private equity and VC firms using AI.
Dili, a Y Combinator graduate, has raised $3.6 million in venture funding to date from Allianz Strategic Investments, Rebel Fund, Singularity Capital, Corenest, Decacorn, Pioneer Fund, NVO Capital, Amino Capital, Rocketship VC, Hi2 Includes Ventures, and Gaming. Hyper Ventures.
“[AI] An investment affects all parts of the fund, from analysts to partners and back-office functions,” Song said. “Professionals investing in funds are looking for a distinct edge in decision-making. are, and now they can use their data to consolidate their understanding of the deal and how it fits into funding … Dili has a unique opportunity to emerge as a solution. Funds in a tough macro environment.”
It’s not wrong to sing about funds looking for an edge — or for that matter any promising new ways to reduce investment risk. VCs reportedly have $311 billion in unspent cash, and last year raised the lowest total in seven years — $67 billion — as they grow increasingly wary of early-stage ventures.
Dili is not the first to apply AI to the due diligence process. Gartner predicts that by 2025, more than 75% of executive reviews of VC and early-stage investors will be informed using AI and data analytics.
Several startups and incumbents are already tapping AI to infuse large amounts of data to produce financial documents and market comparisons and reports—including Vokello (whose customers are private equity and VC funds, such as Dili’s ), Ansarada, AlphaSense and Thomson Reuters (through its Clear Negative Media Unit).
But Song insists the deli uses “first-of-its-kind” technology.
“[We can] He added that it provides much greater accuracy on specific tasks such as extracting financial metrics from large unstructured documents. “We’ve built custom indexing and retrieval pipelines to deliver specific documents. [our AI] Model with high-quality context.”
Dili leverages large language models along the lines of GenAI, specifically OpenAI’s ChatGPT, to streamline investor workflows.
The platform first catalogs a fund’s historical financial data and investment decisions into a knowledge base, and then applies those models to automate tasks such as parsing databases of private company data, applying due diligence Curating lists of and mining unknown personalities on the web. .
Dili recently added support for automated comparative analysis and industry benchmarking on a firm’s backlog of deals. Once funds upload their deal data, they can compare historical and current investment opportunities in one place.
“Imagine being able to receive an email with a new investment opportunity or portfolio company update and instantly have a platform with AI-generated deal red flags, competitive analysis, industry benchmarking and An introductory summary or memo that leverages your fund’s historical investment patterns,” Song. said.
The question is, can Dili’s AI – or indeed any AI – be trusted when it comes to managing a portfolio?
Image credit: The heart
AI is not necessarily known for sticking to facts. Fast Company tested ChatGPT’s ability to summarize articles and found that the model got things wrong, left out pieces, and did not fully describe the details of the invention in the articles it summarized. What is it. It’s not hard to imagine how this could become a real problem in due diligence work, where accuracy is paramount.
AI can also introduce biases into the decision-making process. In an experiment conducted several years ago by the Harvard Business Review, an algorithm trained to make startup investment recommendations was found to select white entrepreneurs over entrepreneurs of color and startups with male founders. I prefer investment. That’s because the public data the algorithm was trained on reflects the fact that fewer women and founders from underrepresented groups are marginalized in the funding process — and ultimately fewer ventures. Raise capital.
Then there’s the fact that some firms may not feel comfortable running their private, sensitive data through a third-party model.
To try to allay these fears, Song said Dili is improving its models — many of which are open source — to reduce the incidence of fraud and improve overall accuracy. It also emphasized that private customer data is not used to train Dili’s models and that Dili plans to offer a method for funds to train on proprietary, offline fund data. Winners make their own models.
While hedge funds and public markets have invested heavily in tech, there is a lot of untapped potential in private market data that Dili can unlock for firms,” Song said.
Dili ran an initial pilot last year with 400 analysts and customers from a variety of funds and banks. But as the startup grows its team and adds new capabilities, it’s looking to expand into new applications — ultimately an “end-to-end” solution for investor due diligence and portfolio management. Towards becoming, says Song.
“Ultimately we believe the underlying technology we are building can be applied to all parts of the asset allocation process,” he added.